Meme Kanseri Tanısında Kullanılan Radyolojik Görüntüleme Yöntemleri ve Moleküler Alt Tiplerin Belirlenmesinde Radyolojinin Rolü
Özet
Meme kanseri tanısında kullanılan temel radyolojik görüntüleme yöntemleri ve tümörün moleküler alt tiplerinin belirlenmesindeki roller incelendiğinde, her modalitenin kendine özgü avantajları öne çıkmaktadır. Tarama ve tanıda altın standart olan mamografi yoğun meme dokusunda duyarlılığını kaybederken, ultrasonografi (US) kistik-solid ayrımı ve aksiller değerlendirmede tamamlayıcı bir rol oynamakta, sonoelastografi ise doku sertliğini ölçerek malignite ayrımına katkı sağlamaktadır. Manyetik Rezonans Görüntüleme (MRG), çok odaklı lezyonların saptanmasında ve cerrahi planlamada en yüksek duyarlılığa sahip "sorun giderici" yöntem olarak kullanılırken, kontrastlı mamografi tümör boyutunu belirlemede MRG ile benzer başarı göstermektedir. Kesici iğne biyopsisi, tedavi planını doğrudan etkileyen histopatolojik ve moleküler sınıflandırmayı (Luminal A, Luminal B, HER2 pozitif ve Triple Negatif) mümkün kılmaktadır. Radyolojik bulgular ile moleküler alt tipler arasında belirgin ilişkiler mevcuttur; iyi prognozlu Luminal tipler genellikle düzensiz/spiküle konturlu kitleler oluştururken, daha agresif olan Triple Negatif tümörler düzgün sınırlı, benign lezyonları taklit eden ancak halkasal kontrastlanma ve yüksek ADC değerleri gösteren yapılar sergilemektedir. Sonuç olarak, gelişen radyolojik modaliteler ve doku analizi (radyomik) yöntemleri, biyopsi öncesi moleküler alt tiplerin öngörülmesinde ve kişiselleştirilmiş tedavi süreçlerinin optimize edilmesinde kritik bir öneme sahiptir.
When the primary radiological imaging modalities used in breast cancer diagnosis and their roles in determining the molecular subtypes of the tumor are examined, the unique advantages of each modality come to the fore. Mammography, the gold standard in screening and diagnosis, loses its sensitivity in dense breast tissue, while ultrasonography (US) plays a complementary role in cystic-solid differentiation and axillary evaluation, and sonoelastography contributes to malignancy differentiation by measuring tissue stiffness. Magnetic Resonance Imaging (MRI) is used as the "problem-solving" method with the highest sensitivity for detecting multifocal lesions and surgical planning, whereas contrast-enhanced mammography demonstrates similar success to MRI in determining tumor size. Core needle biopsy enables histopathological and molecular classification (Luminal A, Luminal B, HER2-positive, and Triple Negative), which directly affects the treatment plan. Significant correlations exist between radiological findings and molecular subtypes; Luminal types with good prognosis usually form irregular/spiculated contoured masses, whereas more aggressive Triple Negative tumors exhibit well-defined structures that mimic benign lesions but display ring enhancement and high ADC values. Consequently, advancing radiological modalities and texture analysis (radiomics) methods hold critical importance in predicting molecular subtypes prior to biopsy and optimizing personalized treatment processes.
Referanslar
Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin 2011; 61:69-90. (PMID: 21296855)
Carol E. DeSantis, Jiemin Ma, Rebecca L. SiegelBreast Cancer Statistics, 2019 CA Cancer J Clin 2019;69:438–451
T.C. Sağlık Bakanlığı. Meme Kanseri Korunma, Tarama, Tanı, Tedavi ve İzlem Klinik Rehberi. Ankara 2020.
Ferlay J, Shin HR, Bray F, et al. Estimates of worldwide burden of cancer in 2008: Globocan 2008. Int J Cancer 2010;127:2893–917.
Aberle DR, Chiles C, Gatsonis C, Hillman BJ, Johnson CD, McClennan BL. Imaging and cancer: research strategy of the American College of Radiology Imaging Network. Radiology 2005;235:741-751.
Mahesh M. AAPM / RSNA Physics Tutorial for Residents Digital Mammography : An Overview Radiographics 2004;24:1747-1760.
Yılmaz, MH. Meme Kanserinde Radyolojik Tanı ve Değerlendirme, İ.Ü. Cerrahpaşa T. F. Sürekli Tıp Eğitimi Etkinlikleri, Meme Kanseri Sempozyum Dizisi No:54, Aralık 27-30, 2006.
Fletcher SW, Elmore JG. Mammographic screening for breast cancer. N Engl J Med 348: 1672-80,2003.
Turgut AT, Hasırcıoğlu F, Koşar U. Meme Hastalıklarının Tanısında Mamografi. STED. 2000;9:12.
D'Orsi CJ, Kopans DB, editors. Mammographic feature analysis. Seminars in roentgenology; 1993: WB Saunders.
Prasad NS, Houserkova D. The Role of Various Modalities in Breast Imaging. Biomed Pap 2007;151:209-218.
Agnese DM. Advances in breast imaging. Surg Technol Int 2005;14:51-56.
Boisserie-Lacroix M, Ranchon N. Contribution of high resolution breast ultrasonography in the characterization of ambiguous mammograms. Gynecol Obstet Fertil 2002;30:147-153.
Ophir J, Céspedes I, Ponnekanti H, Yazdi Y, Li X. Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 13: 111-134, 1991.
Gao L, Parker KJ, Lerner RM, Levinson SF. Imaging of the elastic properties of tissue a review. Ultrasound Med Biol 22: 959-977, 1996.
Chen EJ, Adler RS, Carson PL, Jenkins WK, O’Brien WD Jr. Ultrasound tissue displacement imaging with application to breast cancer. Ultrasound Med Biol 21: 1153-1162, 1995.
Yi A, Cho N, Chang JM, Koo HR, La Yun B, Moon WK. Sonoelastography for 1,786 nonpalpable breast masses: diagnostic value in the decision to biopsy. Eur Radiol 2012; 22:1033–1040
Schaefer FK, Heer I, Schaefer PJ, et al. Breast ultrasound elastography: results of 193 breast lesions in a prospective study with histopathologic correlation. Eur J Radiol 2011; 77:450–456
Scaperrotta G, Ferranti C, Costa C, et al. Role of sonoelastography in nonpalpable breast lesions. Eur Radiol 2008; 18:2381–2389
R. M. Mann, C. K. Kuhl and C. Boetes. Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol (2008) 18: 1307–1318.
Saslow D, Boetes C, Burke W. American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. Cancer Journal for Clinicians 57; 75–89, 2007.
Mann R.M, Kuhl C.K, Kinkel K, Boetes C. Breast MRG: guidelines from the European Society of Breast Imaging. European Radiology 18; 1307–1318, 2008.
Çelik L, Çubuk R. Meme Manyetik Rezonans Görüntüleme: Nasıl, Niçin, Ne Zaman, Kime. Klinik Gelişim 23. 2010;2:6-10.
Cheung YC, Tsai HP, Lo YF, Ueng SH, Huang PC, Chen SC. Clinical utility of dual energy contrast enhanced spectral mammography for breast microcalcifications without associated mass: a preliminary analysis. Eur Radiol 2016;26:1082–1089.
Dromain C, Balleyguier C, Adler G, Garbay JR, Delaloge S. Contrast-enhanced digital mammography. Eur J Radiol 2009;69:34.
Jochelson MS, Dershaw DD, Sung JS, Heerdt AS, Thornton C, Moskowitz CS, et al. Bilateral Contrast enhanced Dual Energy Digital Mammography: Feasibility and Comparison with Conventional Digital Mammography and MR Imaging in Women with Known Breast Carcinoma. Radiology 2013;266:743-751.
Canda T. Meme kanserlerinde prognostik faktörler. Türkiye Ekopatoloji dergisi. 1995;1(1-2):19-36.
Azzopardi J, Chepick O, Hartmann W, Jafarey N, Llombart Bosch A, Ozzello L. The World Health Organization histological typing of breast tumors. second edition. American Journal of Clinical Pathology. 1982;78(6):806-16.
Yang WT, Tse GMK. Sonographic, Mammographic, and Histopathologic Correlation of Symptomatic Ductal Carcinoma In Situ. Am J Roentgenol 126 2004;182:101-110
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen Dale AL, Brown PO,Botstein D. Molecular portraits of human breast tumors. Nature 2000;406:747-752. (PMID: 10963602)
Yersal O, Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol.2014;5(3):412-424.
A. Goldhirsch et al .Strategies for subtypes dealing with the diversity of breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2011 Annals of Oncology, vol. 22, no. 8, pp. 1736– 1747.
Xiaofeng Dai, Ting Li, Zhonghu Bai et al. Breast cancer intrinsic subtype classification, clinical use and future trends . Am J Cancer Res 2015;5(10):2929-2943.
Peng JH, Zhang X, Song JL, Ran L, Luo R, Li HY, Wang YH. Neoadjuvant chemotherapy reduces the expression rates of ER, PR, HER2, Ki 67 and P 53 of invasive ductal carcinoma. Medicine (Baltimore). 2019;98(2):e13554.
Barnes DM, Hanby AM. Oestrogen and progesterone receptors in breast cancer: past, present and future. Histopathology 2001; 38:271-4.
Sørlie T, Perou CM, Tibshirani R.. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001;98:10869–74.
Cheang MC, Chia SK, Voduc D. Ki67 index, HER2 status and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009; 101(10):736–750.
Cancello G, Maisonneuve P, Rotmansz N. Progesterone receptor loss identifies luminal B breast cancer subgroups at higher risk of relapse. Ann Oncol. 2013;24(3):661–668.
Wu VS, Nanaya N, Lo C. From bench to bedside: what do we know about hormone receptor positive and human epidermal growth factor receptor 2 positive breast cancer. J Steroid Biochem Mol Biol. 2015;153:45–53.
Staaf J, Ringner M, Vallon-CHristersson J. Identification of subtypes in human epidermal growth factor receptor 2 positive breast cancer reveals a gene signature prognostic of outcome. J Clin Oncol. 2010;28(11):1813–1820.
Nielsen TO, Hsu FD, Jensen K, Cheang M, Karaca G, Hu Z. Immunohistochemical and clinical characterization of the basal like subtype of invasive breast carcinoma. Clinical cancer research. 2004;10(16):5367-74.
Clark SE, Warwick J, Carpenter R, Bowen RL, Duffy SW, Jones JL. Molecular sub typing of DCIS: heterogenety of breast cancer reflected in preinvasive disease. Br J Cancer 2011;104(1):120–127.
Balleine RL, Webster LR, Davis S. Molecular grading of ductal carcinoma insitu of the breast. Clin Cancer Res 2008;14(24):8244–8252.
Livasy CA, Perou CM, Karaca G. Identification of a basal like subtype of breast ductal carcinoma in situ. Hum Pathol 2007;38(2):197–204.
Balcı P. İnvaziv Kanserler ve Diğer Malign Tümörler. In: Oktay A, editor. Meme Hastalıklarında Görüntüleme. Ankara: Dünya Tıp Kitabevi; 2020. p. 325–48.
Navarro Vilar L, Alandete German SP, Medina Garcia R, Blanc Garcia E, Camarasa Lillo N, Vilar Samper J. MRI Findings in Molecular Subtypes of Breast Cancer According to BIRADS System. Breast J. 2017;23(4):421-8.
Schrading S, Kuhl CK. Mammographic, US, and MRI phenotypes of familial breast cancer. Radiology. 2008;246(1):58-70.
Wojcinski S, Soliman AA, Schmidt J, Makowski L, Degenhardt F, Hillemanns P. Sonographic Features of Triple Negative and NonTriple Negative Breast Cancer. Journal of Ultrasound in Medicine. 2012;31(10):1531-41.
Wang C, Wei W, Santiago L, Whitman G, Dogan B. Can imaging kinetic parameters of dynamic contrast enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer? Acta radiologica (Stockholm, Sweden : 1987). 2018;59(7):813-21.
Montemezzi S, Camera L, Giri MG, Pozzetto A, Calio A, Meliado G. Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer? European journal of radiology. 2018;108:120-7.
Leong LCH, Gombos EC, Jagadeesan J, Fook-Chong SMC. MRI kinetics with volumetric analysis in correlation with hormonal receptor subtypes and histologic grade of invasive breast cancers. AJR AJR 2015;204(3):W348-W56.
Kim SH, Cha ES, Kim HS, Kang BJ, Choi JJ, Jung JH. Diffusion weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. Journal of magnetic resonance imaging : JMRI. 2009;30(3):615-20.
Xie T, Zhao Q, Fu C, Bai Q, Zhou X, Li L. Differentiation of triple negative breast cancer from other subtypes through whole tumor histogram analysis on multiparametric MRI. 2019;29(5):2535-44.
Sharma U, Sah RG, Agarwal K, Parshad R, Seenu V, Mathur SR. Potential of Diffusion Weighted Imaging in the Characterization of Malignant, Benign, and Healthy Breast Tissues and Molecular Subtypes of Breast Cancer. Frontiers in oncology. 2016;6:126.
Montemezzi S, Camera L, Giri MG, Pozzetto A, Calio A, Meliado G. Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer? EJR. 2018;108:120-7.
Jeh SK, Kim SH, Kim HS, Kang BJ, Jeong SH, Yim HW. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. Journal of magnetic resonance imaging : JMRI. 2011;33(1):102-9.
Fan, M., Li, H., Wang, S., Zheng, B., Zhang, J., & Li, L. (2017). Radiomic analysis reveals DCE MRI features for prediction of molecular subtypes of breast cancer. Plos one, 12(2), e0171683.
Holli-Helenius, K., Salminen, A., Rinta-Kiikka, I., Koskivuo, I., Brück, N., Boström, P., & Parkkola, R. 2017. MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes, a feasibility study. BMC Medical Imaging, 17(1)
Chang JM, Won JK, Lee KB, Park IA, Yi A, Moon WK. Comparison of shear wave and strain ultrasound elastography in the differentiation of benign and malignant breast lesions. AJR 2013; 201:W347–W356. 29.
Youk JH, Son EJ, Gweon HM, Kim H, Park YJ, Kim JA. Comparison of strain and shear wave elastography for the differentiation of benign from malignant breast lesions, combined with B mode ultrasonography: qualitative and quantitative assessments. Ultrasound Med Biol 2014;40:2336–2344
Ganau S, Andreu FJ, Escribano F, Martı ´n A, Tortajada L, Villajos M. Shear wave elastography and immunohistochemical profiles in invasive breast cancer: Evaluation of maximum and mean elasticity values. EJR 2015; 84(4):617–22. doi:10.1016/j.ejrad.2014.12.020 PMID: 25619502
Evans A, Whelehan P, Thomson K, McLean D, Brauer K, Purdie C. Invasive breast cancer: relationship between shear wave elastographic findings and histologic prognostic factors. Radiology. 2012; 263(3):673–7. doi: 10.1148/radiol.12111317 PMID: 22523322
Plaza MJ, Handa P, Esserman LE. Preoperative MRI Evaluation of Axillary Lymph Nodes in Invasive Ductal Carcinoma: Comparison of Luminal A Versus Luminal B Subtypes in a Paradigm Using Ki-67 and Receptor Status. AJR. 2017;208(4):910-5.
Howland et al. Lymph Node Involvement in Immunohistochemistry Based Molecular Classifications of Breast Cancer J Surg Res. 2013 December ; 185(2): 697–703.
Yun SJ et al. Risk Stratification For Axillary Lymph Node Metastases in Breast Cancer Patients: What Clinicopathological and Radiological Factors of Primary Breast Cancer Can Predict Preoperatively Axillary Lymph Node Metastases? Ultrasound Quarterly. 2017;33(1):15-22.
Guo Q et al. Ultrasound features of breast cancer for predicting axillary lymp node metastas 2018 Jun;37(6):1354-1353.
